Post-Mortem Framework

SkillMonitoring & ops

Blameless post-mortem structure, incident timeline reconstruction, response evaluation, and organizational learning

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Post-Mortem Framework skill

What this skill tells your AI

The instructions your AI receives, as published by nwave-ai/nwave in nWave/skills/nw-post-mortem-framework/SKILL.md and read by ahel’s review.

Principles

  • Blameless: focus on systems/processes, not individuals. People make reasonable decisions given available info.
  • Evidence-based: every finding backed by logs, metrics, or documented actions
  • Action-oriented: every finding produces concrete, assigned action item
  • Learning-focused: capture what worked alongside what failed

Post-Mortem Document Structure

# Post-Mortem: [Incident Title]

**Date**: [incident date]
**Duration**: [start to resolution]
**Severity**: [P0-P3]
**Author**: [analyst]

## Summary
[2-3 sentence overview: what happened, impact, resolution]

## Timeline
| Time | Event | Source |
|------|-------|--------|
| HH:MM | [event] | [log/metric/report] |

## Impact
- Users affected: [number/percentage]
- Duration of impact: [time]
- Business impact: [quantified if possible]
- Systems affected: [list]

## Root Cause Analysis
[5 Whys analysis with evidence at each level]

## Detection and Response
- Time to detect: [duration] -- [how detected]
- Time to respond: [duration] -- [first action]
- Time to mitigate: [duration] -- [mitigation applied]
- Time to resolve: [duration] -- [permanent fix]

## What Went Well
- [positive observations about detection, response, recovery]

## What Could Be Improved
- [areas where detection, response, recovery fell short]

## Action Items
| ID | Action | Owner | Priority | Due Date |
|----|--------|-------|----------|----------|
| 1 | [specific action] | [team/person] | [P0-P3] | [date] |

## Lessons Learned
- [key takeaways for the organization]

Incident Timeline Reconstruction

Sources

  1. Monitoring alerts/dashboards (timestamps) | 2. Deployment logs/CI-CD records
  2. Communication channels (Slack, email, incident) | 4. VCS (commits, merges, deploys) | 5. User reports/support tickets

Quality Checks

Events chronological with verified timestamps | gaps >5 min noted/explained | decision points identified with available info | causal relationships noted

Response Effectiveness Evaluation

Detection

Detected by monitoring or users? | Duration onset-to-detection? | Existing alerts relevant? Missing?

Escalation

Right team at right time? | Procedures followed? | Communication clear to stakeholders?

Resolution

Mitigation effective? | Rollback considered/viable? | Duration mitigation-to-permanent-fix?

Organizational Learning

Knowledge Capture

Document root causes as reusable patterns | update runbooks | share in retrospectives

Process Improvements

Update monitoring/alerting per detection gaps | revise deployment per rollback effectiveness | strengthen testing for failure scenario

Action Item Tracking

Every item has owner + due date | track in standups/sprint reviews | verify effectiveness post-deployment

Signals

GitHub stars
610
Forks
64
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
nw-post-mortem-framework
Source
github.com/nwave-ai/nwave